Azure Monitor 和 IoTDB 集成

通过 InfluxData 构建的开源数据连接器 Telegraf,实现强大的性能和简单的集成。

info

对于大规模实时查询,这不是推荐的配置。为了实现查询和压缩优化、高速摄取和高可用性,您可能需要考虑Azure Monitor 和 InfluxDB

50 亿+

Telegraf 下载量

#1

时间序列数据库
来源:DB Engines

10 亿+

InfluxDB 下载量

2,800+

贡献者

目录

强大的性能,无限的扩展

收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它都会更有价值。InfluxDB 是排名第一的时间序列平台,旨在与 Telegraf 一起扩展。

查看入门方法

输入和输出集成概述

使用 Azure Monitor API 从 Azure 资源收集指标。

此插件将 Telegraf 指标保存到 Apache IoTDB 后端,支持会话连接和数据插入。

集成详情

Azure Monitor

Azure Monitor Telegraf 插件专门用于使用 Azure Monitor API 从各种 Azure 资源收集指标。用户必须提供特定的凭据,例如 client_idclient_secrettenant_idsubscription_id,以进行身份验证并获得对其 Azure 资源的访问权限。此外,该插件还支持从单个资源以及资源组或订阅收集指标的功能,从而可以灵活且可扩展地收集根据用户需求定制的指标。此插件非常适合利用 Azure 云基础设施的组织,可以深入了解资源性能和随时间推移的利用率,从而促进云资源的主动管理和优化。

IoTDB

Apache IoTDB(物联网数据库)是一种 IoT 原生数据库,具有高性能的数据管理和分析能力,可部署在边缘和云端。其轻量级架构、高性能和丰富的功能集非常适合物联网工业领域中的海量数据存储、高速数据摄取和复杂分析。IoTDB 与 Apache Hadoop、Spark 和 Flink 深度集成,进一步增强了其处理大规模数据和复杂处理任务的能力。

配置

Azure Monitor

# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
  # can be found under Overview->Essentials in the Azure portal for your application/service
  subscription_id = "<>"
  # can be obtained by registering an application under Azure Active Directory
  client_id = "<>"
  # can be obtained by registering an application under Azure Active Directory.
  # If not specified Default Azure Credentials chain will be attempted:
  # - Environment credentials (AZURE_*)
  # - Workload Identity in Kubernetes cluster
  # - Managed Identity
  # - Azure CLI auth
  # - Developer Azure CLI auth
  client_secret = "<>"
  # can be found under Azure Active Directory->Properties
  tenant_id = "<>"
  # Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic.
  # cloud_option = "AzurePublic"

  # resource target #1 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    # can be found under Overview->Essentials->JSON View in the Azure portal for your application/service
    # must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx'
    # must be removed from the beginning of Resource ID property value)
    resource_id = "<>"
    # the metric names to collect
    # leave the array empty to use all metrics available to this resource
    metrics = [ "<>", "<>" ]
    # metrics aggregation type value to collect
    # can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum'
    # leave the array empty to collect all aggregation types values for each metric
    aggregations = [ "<>", "<>" ]

  # resource target #2 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    resource_id = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # resource group target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    # the resource group name
    resource_group = "<>"

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      # the resource type
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # resource group target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    resource_group = "<>"

    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # subscription target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # subscription target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]
</code></pre>

IoTDB

[[outputs.iotdb]]
  ## Configuration of IoTDB server connection
  host = "127.0.0.1"
  # port = "6667"

  ## Configuration of authentication
  # user = "root"
  # password = "root"

  ## Timeout to open a new session.
  ## A value of zero means no timeout.
  # timeout = "5s"

  ## Configuration of type conversion for 64-bit unsigned int
  ## IoTDB currently DOES NOT support unsigned integers (version 13.x).
  ## 32-bit unsigned integers are safely converted into 64-bit signed integers by the plugin,
  ## however, this is not true for 64-bit values in general as overflows may occur.
  ## The following setting allows to specify the handling of 64-bit unsigned integers.
  ## Available values are:
  ##   - "int64"       --  convert to 64-bit signed integers and accept overflows
  ##   - "int64_clip"  --  convert to 64-bit signed integers and clip the values on overflow to 9,223,372,036,854,775,807
  ##   - "text"        --  convert to the string representation of the value
  # uint64_conversion = "int64_clip"

  ## Configuration of TimeStamp
  ## TimeStamp is always saved in 64bits int. timestamp_precision specifies the unit of timestamp.
  ## Available value:
  ## "second", "millisecond", "microsecond", "nanosecond"(default)
  # timestamp_precision = "nanosecond"

  ## Handling of tags
  ## Tags are not fully supported by IoTDB.
  ## A guide with suggestions on how to handle tags can be found here:
  ##     https://iotdb.apache.org/UserGuide/Master/API/InfluxDB-Protocol.html
  ##
  ## Available values are:
  ##   - "fields"     --  convert tags to fields in the measurement
  ##   - "device_id"  --  attach tags to the device ID
  ##
  ## For Example, a metric named "root.sg.device" with the tags `tag1: "private"`  and  `tag2: "working"` and
  ##  fields `s1: 100`  and `s2: "hello"` will result in the following representations in IoTDB
  ##   - "fields"     --  root.sg.device, s1=100, s2="hello", tag1="private", tag2="working"
  ##   - "device_id"  --  root.sg.device.private.working, s1=100, s2="hello"
  # convert_tags_to = "device_id"

  ## Handling of unsupported characters
  ## Some characters in different versions of IoTDB are not supported in path name
  ## A guide with suggetions on valid paths can be found here:
  ## for iotdb 0.13.x           -> https://iotdb.apache.org/UserGuide/V0.13.x/Reference/Syntax-Conventions.html#identifiers
  ## for iotdb 1.x.x and above  -> https://iotdb.apache.org/UserGuide/V1.3.x/User-Manual/Syntax-Rule.html#identifier
  ##
  ## Available values are:
  ##   - "1.0", "1.1", "1.2", "1.3"  -- enclose in `` the world having forbidden character 
  ##                                    such as @ $ # : [ ] { } ( ) space
  ##   - "0.13"                      -- enclose in `` the world having forbidden character 
  ##                                    such as space
  ##
  ## Keep this section commented if you don't want to sanitize the path
  # sanitize_tag = "1.3"

输入和输出集成示例

Azure Monitor

  1. 动态资源监控:使用 Azure Monitor 插件根据特定条件(如标签或资源类型)动态收集 Azure 资源的指标。组织可以自动化加载和卸载资源指标的过程,从而根据资源利用率模式实现更好的性能跟踪和优化。

  2. 多云监控集成:将从 Azure Monitor 收集的指标与其他云提供商集成,使用集中式监控解决方案。这使组织能够查看和分析跨多个云部署的性能数据,从而全面了解资源性能和成本,并简化运营。

  3. 异常检测和警报:结合机器学习算法,利用通过 Azure Monitor 插件收集的指标来检测资源利用率中的异常。通过建立基线性能指标并自动发出偏差警报,组织可以在风险升级之前减轻风险并解决性能问题。

  4. 历史性能分析:通过将收集的 Azure 指标馈送到数据仓库解决方案中,使用收集的 Azure 指标进行历史分析。这使组织能够跟踪随时间推移的趋势,从而根据历史性能数据进行详细报告和决策。

IoTDB

  1. 实时物联网监控:利用 IoTDB 插件从各种 IoT 设备收集传感器数据,并将其保存在 Apache IoTDB 后端,从而实现对温度和湿度等环境条件的实时监控。此用例使组织能够分析随时间推移的趋势并根据历史数据做出明智的决策,同时还可以利用 IoTDB 的高效存储和查询功能。

  2. 智能农业数据收集:使用 IoTDB 插件从部署在田地的智能农业传感器收集指标。通过将湿度水平、养分含量和大气条件传输到 IoTDB,农民可以访问有关最佳种植和浇水计划的详细见解,从而提高作物产量和资源管理水平。

  3. 能源消耗分析:利用 IoTDB 插件跟踪来自公用事业网络中智能电表的能源消耗指标。这种集成使分析能够识别使用高峰并预测未来的消耗模式,最终支持节能措施和改进的公用事业管理。

  4. 自动化工业设备监控:使用此插件收集制造工厂中机器的运行指标,并将其存储在 IoTDB 中以进行分析。此设置可以帮助识别效率低下、预测性维护需求和运行异常,从而确保最佳性能并最大限度地减少意外停机时间。

反馈

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强大的性能,无限的扩展

收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它都会更有价值。InfluxDB 是排名第一的时间序列平台,旨在与 Telegraf 一起扩展。

查看入门方法

相关集成

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HTTP 插件从一个或多个 HTTP(S) 端点收集指标。它支持各种身份验证方法和数据格式的配置选项。

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Kafka 和 InfluxDB 集成

此插件从 Kafka 读取消息,并允许根据这些消息创建指标。它支持各种配置,包括不同的 Kafka 设置和消息处理选项。

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Kinesis 和 InfluxDB 集成

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